Localization v2 (#52)

* Add Leonard's new localization models

* Add back existing model

* Fix anchor sizes for mobilenet model

* Update new model names & descriptions.

* Use new object detector in tests

* Update expected test results

trapdata/common/utils.py

@@ -28,7 +28,7 @@ def slugify(s):
     # Quick method to make an acceptable attribute name or url part from a title
     # install python-slugify for handling unicode chars, numbers at the beginning, etc.
     separator = "_"
-    acceptable_chars = list(string.ascii_letters) + [separator]
+    acceptable_chars = list(string.ascii_letters) + list(string.digits) + [separator]
     return (
         "".join(
             [

trapdata/ml/models/localization.py

@@ -3,7 +3,10 @@
 import PIL.Image
 import torch
 import torchvision
-from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
+import torchvision.models.detection.anchor_utils
+import torchvision.models.detection.backbone_utils
+import torchvision.models.detection.faster_rcnn
+import torchvision.models.mobilenetv3
 
 from trapdata import TrapImage, db, logger
 from trapdata.db.models.detections import save_detected_objects
@@ -147,7 +150,7 @@ def save_results(self, item_ids, batch_output):
         )
 
 
-class MothObjectDetector_FasterRCNN(ObjectDetector):
+class MothObjectDetector_FasterRCNN_2021(ObjectDetector):
     name = "FasterRCNN for AMI Moth Traps 2021"
     weights_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/localization/v1_localizmodel_2021-08-17-12-06.pt"
     description = (
@@ -160,7 +163,11 @@ def get_model(self):
         model = torchvision.models.detection.fasterrcnn_resnet50_fpn(weights=None)
         num_classes = 2  # 1 class (object) + background
         in_features = model.roi_heads.box_predictor.cls_score.in_features
-        model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
+        model.roi_heads.box_predictor = (
+            torchvision.models.detection.faster_rcnn.FastRCNNPredictor(
+                in_features, num_classes
+            )
+        )
         logger.debug(f"Loading weights: {self.weights}")
         checkpoint = torch.load(self.weights, map_location=self.device)
         state_dict = checkpoint.get("model_state_dict") or checkpoint
@@ -186,34 +193,92 @@ def post_process_single(self, output):
         return bboxes
 
 
-class GenericObjectDetector_FasterRCNN_MobileNet(ObjectDetector):
-    name = "Pre-trained FasterRCNN with MobileNet backend"
+class MothObjectDetector_FasterRCNN_2023(ObjectDetector):
+    name = "FasterRCNN for AMI Moth Traps 2023"
+    weights_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/localization/fasterrcnn_resnet50_fpn_tz53qv9v.pt"
     description = (
-        "Faster version of FasterRCNN but not trained on moth trap data. "
-        "Produces multiple overlapping bounding boxes. But helpful for testing on CPU machines."
+        "Model trained on GBIF images and synthetic data in 2023. "
+        "Accurate but can be slow on a machine without GPU."
     )
-    bbox_score_threshold = 0.01
+    bbox_score_threshold = 0.80
 
     def get_model(self):
-        model = torchvision.models.detection.fasterrcnn_mobilenet_v3_large_fpn(
-            weights="DEFAULT"
+        num_classes = 2  # 1 class (object) + background
+        logger.debug(f"Loading weights: {self.weights}")
+        model = torchvision.models.get_model(
+            name="fasterrcnn_resnet50_fpn",
+            num_classes=num_classes,
+            pretrained=False,
         )
-        # @TODO can I use load_state_dict here with weights="DEFAULT"?
+        checkpoint = torch.load(self.weights, map_location=self.device)
+        state_dict = checkpoint.get("model_state_dict") or checkpoint
+        model.load_state_dict(state_dict)
         model = model.to(self.device)
         model.eval()
-        return model
+        self.model = model
+        return self.model
 
     def post_process_single(self, output):
         # This model does not use the labels from the object detection model
         _ = output["labels"]
+        assert all([label == 1 for label in output["labels"]])
 
         # Filter out objects if their score is under score threshold
-        bboxes = output["boxes"][
-            (output["scores"] > self.bbox_score_threshold) & (output["labels"] > 1)
-        ]
+        bboxes = output["boxes"][output["scores"] > self.bbox_score_threshold]
 
-        # Filter out background label, if using pretrained model only!
-        bboxes = output["boxes"][output["labels"] > 1]
+        logger.debug(
+            f"Keeping {len(bboxes)} out of {len(output['boxes'])} objects found (threshold: {self.bbox_score_threshold})"
+        )
+
+        bboxes = bboxes.cpu().numpy().astype(int).tolist()
+        return bboxes
+
+
+class MothObjectDetector_FasterRCNN_MobileNet_2023(ObjectDetector):
+    name = "FasterRCNN - MobileNet for AMI Moth Traps 2023"
+    weights_path = "https://object-arbutus.cloud.computecanada.ca/ami-models/moths/localization/fasterrcnn_mobilenet_v3_large_fpn_uqfh7u9w.pt"
+    description = (
+        "Model trained on GBIF images and synthetic data in 2023. "
+        "Slightly less accurate but much faster than other models."
+    )
+    bbox_score_threshold = 0.50
+    trainable_backbone_layers = 6  # all layers are trained
+    anchor_sizes = (64, 128, 256, 512)
+    num_classes = 2
+
+    def get_model(self):
+        norm_layer = torch.nn.BatchNorm2d
+        backbone = torchvision.models.mobilenetv3.mobilenet_v3_large(
+            weights=None, norm_layer=norm_layer
+        )
+        backbone = torchvision.models.detection.backbone_utils._mobilenet_extractor(
+            backbone, True, self.trainable_backbone_layers
+        )
+        anchor_sizes = (self.anchor_sizes,) * 3
+        aspect_ratios = ((0.5, 1.0, 2.0),) * len(anchor_sizes)
+        model = torchvision.models.detection.faster_rcnn.FasterRCNN(
+            backbone,
+            self.num_classes,
+            rpn_anchor_generator=torchvision.models.detection.anchor_utils.AnchorGenerator(
+                anchor_sizes, aspect_ratios
+            ),
+            rpn_score_thresh=0.05,
+        )
+        checkpoint = torch.load(self.weights, map_location=self.device)
+        state_dict = checkpoint.get("model_state_dict") or checkpoint
+        model.load_state_dict(state_dict)
+        model = model.to(self.device)
+        model.eval()
+        self.model = model
+        return self.model
+
+    def post_process_single(self, output):
+        # This model does not use the labels from the object detection model
+        _ = output["labels"]
+        assert all([label == 1 for label in output["labels"]])
+
+        # Filter out objects if their score is under score threshold
+        bboxes = output["boxes"][output["scores"] > self.bbox_score_threshold]
 
         logger.debug(
             f"Keeping {len(bboxes)} out of {len(output['boxes'])} objects found (threshold: {self.bbox_score_threshold})"